if (!require("pacman")) install.packages("pacman")
## Loading required package: pacman
library(pacman)
p_load(imager, wavethresh, ggplot2, dplyr, SpatialPack)
Gaussiano
image_1_path <- file.path("fotos", "1.JPG")
image_1_original_cimg <- load.image(image_1_path)
image_1_fixed_cimg <- drop(image_1_original_cimg)
set.seed(123)
noise_std_dev <- 0.5
image_1_array <- as.array(image_1_fixed_cimg)
image_1_dims <- dim(image_1_array)
noise_gaussian <- array(
rnorm(prod(image_1_dims),
mean = 0,
sd = noise_std_dev
),
dim = dim(image_1_array)
)
image_1_noisy_array <- pmax(0, pmin(1, image_1_array + noise_gaussian))
image_1_noisy_cimg <- as.cimg(
image_1_noisy_array,
x = image_1_dims[1],
y = image_1_dims[2],
cc = image_1_dims[3],
z = 1
)
layout(matrix(1:1, 1, 1))
plot(image_1_original_cimg, main = "Original")
plot(image_1_noisy_cimg, main = "Gaussian Noise")
rm(
image_1_original_cimg,
image_1_fixed_cimg,
image_1_array,
noise_gaussian,
image_1_noisy_array,
image_1_noisy_cimg
)
Sinusoidal con alta frecuencia
image_2_path <- file.path("fotos", "2.JPG")
image_2_original_cimg <- load.image(image_2_path)
image_2_fixed_cimg <- drop(image_2_original_cimg)
image_2_array <- as.array(image_2_fixed_cimg)
frequency <- 25 # Frequency of sinusoidal noise
amplitude <- 0.2 # Amplitude of noise
height <- dim(image_2_array)[1]
width <- dim(image_2_array)[2]
channels_original_2 <- lapply(1:3, function(i) as.vector(image_2_array[, , i]))
noise_sinusoidal_alta <- lapply(channels_original_2, function(channel) {
x <- seq(0, 2 * pi, length.out = width)
y <- seq(0, 2 * pi, length.out = height)
noise_grid <- outer(sin(x * frequency), sin(y * frequency))
noise_vector <- as.vector(noise_grid) * amplitude
noise_vector
})
channels_noisy_2 <- Map(function(channel, n) pmax(0, pmin(1, channel + n)), channels_original_2, noise_sinusoidal_alta)
image_2_noisy_cimg <- as.cimg(
array(
unlist(channels_noisy_2),
dim = c(dim(image_2_fixed_cimg)[1:2], 1, 3)
)
)
layout(matrix(1:1, 1, 1))
plot(image_2_original_cimg, main = "Original")
plot(image_2_noisy_cimg, main = "Sinusoidal con alta frecuencia")
rm(
image_2_original_cimg,
channels_noisy_2,
channels_original_2,
image_2_array,
noise_sinusoidal_alta,
image_2_noisy_cimg
)
Sinusoidal con baja frecuencia
image_3_path <- file.path("fotos", "3.JPG")
image_3_original_cimg <- load.image(image_3_path)
image_3_fixed_cimg <- drop(image_3_original_cimg)
image_3_array <- as.array(image_3_fixed_cimg)
frequency <- 2 # Frequency of sinusoidal noise
amplitude <- 0.2 # Amplitude of noise
height <- dim(image_3_array)[1]
width <- dim(image_3_array)[2]
channels_original_3 <- lapply(1:3, function(i) as.vector(image_3_array[, , i]))
noise_sinusoidal_baja <- lapply(channels_original_3, function(channel) {
x <- seq(0, 2 * pi, length.out = width)
y <- seq(0, 2 * pi, length.out = height)
noise_grid <- outer(sin(x * frequency), sin(y * frequency))
noise_vector <- as.vector(noise_grid) * amplitude
noise_vector
})
channels_noisy_3 <- Map(function(channel, n) pmax(0, pmin(1, channel + n)), channels_original_3, noise_sinusoidal_baja)
image_3_noisy_cimg <- as.cimg(
array(
unlist(channels_noisy_3),
dim = c(dim(image_3_fixed_cimg)[1:2], 1, 3)
)
)
layout(matrix(1:1, 1, 1))
plot(image_3_original_cimg, main = "Original")
plot(image_3_noisy_cimg, main = "Sinusoidal con baja frecuencia")
rm(
image_3_original_cimg,
image_3_fixed_cimg,
image_3_array,
channels_original_3,
channels_noisy_3,
noise_sinusoidal_baja,
image_3_noisy_cimg
)
Salt & Pepper Noise
image_4_path <- file.path("fotos", "4.JPG")
image_4_original_cimg <- load.image(image_4_path)
image_4_fixed_cimg <- drop(image_4_original_cimg)
image_4_array <- as.array(image_4_fixed_cimg)
channels_noisy_4 <- lapply(1:3, function(channel) {
channel_matrix <- image_4_array[, , channel]
noise_channel <- SpatialPack::imnoise(
img = channel_matrix,
type = "saltnpepper",
epsilon = 0.2
)
return(noise_channel)
})
image_4_noisy_array <- array(
unlist(channels_noisy_4),
dim = dim(image_4_fixed_cimg)
)
image_4_noisy_cmg <- as.cimg(image_4_noisy_array)
layout(matrix(1:1, 1, 1))
plot(as.cimg(image_4_original_cimg), main = "Original")
plot(image_4_noisy_cmg, main = "Salt & Pepper Noise")
rm(
image_4_original_cimg,
image_4_fixed_cimg,
image_4_array,
channels_noisy_4,
image_4_noisy_array,
image_4_noisy_cimg
)
Gamma Noise
image_5_path <- file.path("fotos", "5.JPG")
image_5_original_cimg <- load.image(image_5_path)
image_5_fixed_cimg <- drop(image_5_original_cimg)
image_5_array <- as.array(image_5_fixed_cimg)
channels_noisy_5 <- lapply(1:3, function(channel) {
channel_matrix <- image_5_array[, , channel]
noise_channel <- SpatialPack::imnoise(
img = channel_matrix,
type = "gamma",
looks = 2
)
return(noise_channel)
})
image_5_noisy_array <- array(
unlist(channels_noisy_5),
dim = dim(image_5_fixed_cimg)
)
image_5_noisy_cimg <- as.cimg(image_5_noisy_array)
layout(matrix(1:1, 1, 1))
plot(image_5_original_cimg, main = "Original")
plot(image_5_noisy_cimg, main = "Gamma Noise")
rm(
image_5_original_cimg,
image_5_fixed_cimg,
image_5_array,
channels_noisy_5,
image_5_noisy_array,
image_5_noisy_cimg
)
